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Predicting HLA CD4 Immunogenicity in Human Populations

BACKGROUND: Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key...

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Autores principales: Dhanda, Sandeep Kumar, Karosiene, Edita, Edwards, Lindy, Grifoni, Alba, Paul, Sinu, Andreatta, Massimo, Weiskopf, Daniela, Sidney, John, Nielsen, Morten, Peters, Bjoern, Sette, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010533/
https://www.ncbi.nlm.nih.gov/pubmed/29963059
http://dx.doi.org/10.3389/fimmu.2018.01369
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author Dhanda, Sandeep Kumar
Karosiene, Edita
Edwards, Lindy
Grifoni, Alba
Paul, Sinu
Andreatta, Massimo
Weiskopf, Daniela
Sidney, John
Nielsen, Morten
Peters, Bjoern
Sette, Alessandro
author_facet Dhanda, Sandeep Kumar
Karosiene, Edita
Edwards, Lindy
Grifoni, Alba
Paul, Sinu
Andreatta, Massimo
Weiskopf, Daniela
Sidney, John
Nielsen, Morten
Peters, Bjoern
Sette, Alessandro
author_sort Dhanda, Sandeep Kumar
collection PubMed
description BACKGROUND: Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides. METHODS: Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an “immunogenicity score.” We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level. RESULTS: The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore). CONCLUSION: The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.
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spelling pubmed-60105332018-06-29 Predicting HLA CD4 Immunogenicity in Human Populations Dhanda, Sandeep Kumar Karosiene, Edita Edwards, Lindy Grifoni, Alba Paul, Sinu Andreatta, Massimo Weiskopf, Daniela Sidney, John Nielsen, Morten Peters, Bjoern Sette, Alessandro Front Immunol Immunology BACKGROUND: Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides. METHODS: Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an “immunogenicity score.” We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level. RESULTS: The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore). CONCLUSION: The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification. Frontiers Media S.A. 2018-06-14 /pmc/articles/PMC6010533/ /pubmed/29963059 http://dx.doi.org/10.3389/fimmu.2018.01369 Text en Copyright © 2018 Dhanda, Karosiene, Edwards, Grifoni, Paul, Andreatta, Weiskopf, Sidney, Nielsen, Peters and Sette. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Dhanda, Sandeep Kumar
Karosiene, Edita
Edwards, Lindy
Grifoni, Alba
Paul, Sinu
Andreatta, Massimo
Weiskopf, Daniela
Sidney, John
Nielsen, Morten
Peters, Bjoern
Sette, Alessandro
Predicting HLA CD4 Immunogenicity in Human Populations
title Predicting HLA CD4 Immunogenicity in Human Populations
title_full Predicting HLA CD4 Immunogenicity in Human Populations
title_fullStr Predicting HLA CD4 Immunogenicity in Human Populations
title_full_unstemmed Predicting HLA CD4 Immunogenicity in Human Populations
title_short Predicting HLA CD4 Immunogenicity in Human Populations
title_sort predicting hla cd4 immunogenicity in human populations
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010533/
https://www.ncbi.nlm.nih.gov/pubmed/29963059
http://dx.doi.org/10.3389/fimmu.2018.01369
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